Abstract About 700,000 to 1 million falls per year occur during hospital stays in the US, costing hospitals $3 billion to $7 billion each year to treat. These costs are not reimbursed by the Centers of Medicare and Medicaid, resulting in significant financial losses for hospitals. Delirium, a major cause of falls, is highly prevalent (29%-64%) among hospitalized elders. Even without causing a fall, the average case of delirium increases the length of stay by 7.78 days, disrupting throughput and significantly reducing net hospital revenues. Including the common sequelae of post-discharge functional decline, delirium costs the national healthcare system over $130 billion per year. Due to the high cost and significant psychosocial needs of certain hospitalized patients, a widely-used intervention to mitigate risk is to assign hospital staff & nurses to serve as ?patient sitters? at the bedside, at a cost of $1 million/year for a typical hospital. However, despite the workforce burden and expense, patient sitters often do not reliably execute risk-mitigating protocols, and the literature does not support their efficacy in preventing adverse events. In this SBIR Fast-Track proposal, we seek to develop an advanced, human-in-the-loop artificial intelligence (AI) avatar system to enhance the wellbeing of hospitalized patients, avoid adverse events including delirium and falls, and improve workforce efficiency by supporting nursing staff to work at the top of their license, potentially generating savings of $2,000,000 each year for a typical 300-bed hospital. This proposal aligns with NINR?s cross-cutting focus areas of Promoting Innovation and 21st Century Nurse Scientists, while applying the principles of patient self-management and wellness to the acute care environment, where the outcomes driven by our patient engagement and support platform will have an outsized, immediate cost benefit to enable rapid scaling and dissemination.